Remote sensing to characterise vegetation fuel moisture content in the UK uplands

Wildfires are an important hazard globally as they lead to significant land degradation, carbon losses and impact on human activities. Recent research has demonstrated how dynamic fire risk estimates can be informed by the use of remote sensing technology. The focus here is on improving methods for fire risk evaluation, so that prediction about where and when fires are likely to start can become more accurate. Fuel moisture content (FMC) is one of the most important factors influencing wildfire risk, as it controls the probability of ignition and the rate of spread of a fire. This work aims to assess the potential of calibrated time-series Sentinel-2A MultiSpectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) data to estimate and map FMC in upland areas of the UK. The work employs laboratory and field-scale measurements, and radiative transfer modelling, to test the relationships between reflectance and FMC. Calluna vulgaris samples were collected from a test site in the UK Peak District, and their FMC determined. Near-coincident multi-temporal satellite imagery was acquired for the test site and maps of FMC generated using relationships tested through the laboratory work and modelling. The results showed a strong relationship between the normalized difference water index (NDWI) and moisture stress index (MSI) with FMC, which was independent of scale. The relationship was not strongly affected by variations in soil background properties or differences in solar zenith angle. Spatial mapping of FMC across the Peak District National Park revealed temporal and spatial variations in FMC in Calluna-dominated areas. The results have implications for wildfire risk management and for upland vegetation management and conservation.

Level dc:type.qualificationlevel

Doctoral (Level 8)

Year dc:date.issued

2026

Author dc:creator

• Badi, AH

Language dc:language

en

Identifier

oai:salford-repository.worktribe.com:1365373

OAI identifier oai:identifier

oai:salford-repository.worktribe.com:1365373